{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day08 基础构图元素：Spines\n",
    "\n",
    "　　今天我们来学习有关`matplotlib`中`Spines`的知识，它其实就是我们默认绘图时，图像区域上下左右的类似边框线的元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:55.375840Z",
     "iopub.status.busy": "2020-10-21T10:57:55.375840Z",
     "iopub.status.idle": "2020-10-21T10:57:55.558344Z",
     "shell.execute_reply": "2020-10-21T10:57:55.558344Z",
     "shell.execute_reply.started": "2020-10-21T10:57:55.375840Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.3.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模块\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 设定默认字体为SimHei以解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决图像负号'-'不正常显示的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　`matplotlib`中的`Spines`按照上下左右依次可分为`top`、`bottom`、`left`以及`right`，要提取对应方位的`Spines`，直接`ax.spines[位置]`填入对应方位即可，譬如我们基于`Spines`对象的`set_color()`方法，可以设置对应`Spines`的色彩，传入`'none'`即为无色彩："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:55.559341Z",
     "iopub.status.busy": "2020-10-21T10:57:55.559341Z",
     "iopub.status.idle": "2020-10-21T10:57:55.734906Z",
     "shell.execute_reply": "2020-10-21T10:57:55.734906Z",
     "shell.execute_reply.started": "2020-10-21T10:57:55.559341Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.linspace(-np.pi, np.pi, 100)\n",
    "y = 2 * np.sin(x)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, y)\n",
    "\n",
    "# 隐藏右侧与顶部Spines\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　而关于`Spines`，我们运用最多的设置是调整它们的位置，使用`Spines`对象的`set_position()`可以非常自由地调整对应`Spines`的位置，其接受的参数为二元组，有以下几种输入格式：\n",
    "\n",
    "- `('outward', points)`\n",
    "\n",
    "　　在二元组的第一个位置传入`'outward'`，就代表其相对原始默认位置向外的由`points`指定的像素偏移量，设置为负数即为向内偏移："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:55.736866Z",
     "iopub.status.busy": "2020-10-21T10:57:55.735869Z",
     "iopub.status.idle": "2020-10-21T10:57:55.854483Z",
     "shell.execute_reply": "2020-10-21T10:57:55.854483Z",
     "shell.execute_reply.started": "2020-10-21T10:57:55.736866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, y)\n",
    "\n",
    "ax.spines['top'].set_position(('outward', 50))\n",
    "ax.spines['right'].set_position(('outward', -50));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `('axes', 偏移比例)`\n",
    "\n",
    "　　在二元组的第一个位置传入`'axes'`，第二个位置传入0-1之间的数值，代表对应`Spines`对应方向的位置，因为整个`Axes`内部从左往右是0到1，从下往上是0-1的方向，譬如`right`对应的`Spines`所在的原始位置就是1，通过减小它就可以往左边移动，下面我们来移动4个方向的`Spines`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:55.856480Z",
     "iopub.status.busy": "2020-10-21T10:57:55.855516Z",
     "iopub.status.idle": "2020-10-21T10:57:55.981180Z",
     "shell.execute_reply": "2020-10-21T10:57:55.981180Z",
     "shell.execute_reply.started": "2020-10-21T10:57:55.856480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, y, clip_on=False)\n",
    "\n",
    "# 把每个方位的Spines都往对应反向位置移动25%\n",
    "ax.spines['top'].set_position(('axes', 0.75))\n",
    "ax.spines['right'].set_position(('axes', 0.75))\n",
    "ax.spines['left'].set_position(('axes', 0.25))\n",
    "ax.spines['bottom'].set_position(('axes', 0.25));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `('data', 坐标轴位置)`\n",
    "\n",
    "　　在二元组的第一个位置传入`'data'`时，我们就可以基于坐标轴真实的坐标进行`Spines`位置的调整，譬如我们把`left`对应的`Spines`挪动到0位置："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:55.982143Z",
     "iopub.status.busy": "2020-10-21T10:57:55.982143Z",
     "iopub.status.idle": "2020-10-21T10:57:56.094877Z",
     "shell.execute_reply": "2020-10-21T10:57:56.094877Z",
     "shell.execute_reply.started": "2020-10-21T10:57:55.982143Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, y, clip_on=False)\n",
    "\n",
    "ax.spines['left'].set_position(('data', 0))\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['right'].set_color('none');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:56.095882Z",
     "iopub.status.busy": "2020-10-21T10:57:56.095882Z",
     "iopub.status.idle": "2020-10-21T10:57:56.202635Z",
     "shell.execute_reply": "2020-10-21T10:57:56.202635Z",
     "shell.execute_reply.started": "2020-10-21T10:57:56.095882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, y, clip_on=False)\n",
    "\n",
    "ax.spines['left'].set_bounds((0, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　今天的任务请你在下面所给绘图代码的基础上，结合今天以及过往所学知识，绘制出下面的图像：\n",
    " \n",
    "<center>\n",
    "  <img src=\"imgs/demo.png\" width=400></img>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T10:57:56.203578Z",
     "iopub.status.busy": "2020-10-21T10:57:56.203578Z",
     "iopub.status.idle": "2020-10-21T10:57:56.307275Z",
     "shell.execute_reply": "2020-10-21T10:57:56.307275Z",
     "shell.execute_reply.started": "2020-10-21T10:57:56.203578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import ticker\n",
    "x = np.linspace(-2, 2, 401)\n",
    "y1 = np.sqrt(1-(np.abs(x)-1)*(np.abs(x)-1))\n",
    "y2 = np.arccos(1-np.abs(x))-np.pi\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "#设置spines\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['bottom'].set_color('none')\n",
    "ax.spines['left'].set_position(('data', 0));\n",
    "#隐藏xy轴标签\n",
    "ax.yaxis.set_major_locator(ticker.NullLocator())\n",
    "ax.xaxis.set_major_locator(ticker.NullLocator());\n",
    "ax.plot(x, y1, color = 'r')\n",
    "ax.plot(x, y2, color = 'r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 请在今天对应的帖子下回复你的文字代码及绘图结果"
   ]
  }
 ],
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   "display_name": "Python 3",
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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